Quantification of Degree of Liver Fibrosis Using Fibrosis Area Fraction Based on Statistical Chi-Square Analysis of Heterogeneity of Liver Tissue Texture on Routine Ultrasound Images

Publication date: Available online 2 November 2018

Source: Academic Radiology

Author(s): Janelle Li, Mustafa Qureshi, Avneesh Gupta, Stephan W. Anderson, Jorge Soto, Baojun Li

Rationale and Objectives

We present a novel method to quantify the degree of liver fibrosis using fibrosis area fraction based on statistical chi-square analysis of heterogeneity of echo texture within liver on routine ultrasound images. We demonstrate, in a clinical study, that fibrosis area fraction derived this way has the potential to become a noninvasive, quantitative radiometric discriminator of normal or low-grade liver fibrosis (Ishak fibrosis score range = F0–3) and advanced liver fibrosis or cirrhosis (Ishak fibrosis score range = F4–6) on routine ultrasound images.

Materials and Methods

This retrospective patient study was institutional review board approved. Ultrasound images of 100 patients (61 males, 39 females; 18–81 years) who underwent nontargeted ultrasound-guided biopsy were randomly divided into two groups: a training group consisted of 31 cases, and a validation group that contained the rest cases. An investigator manually selected a primary region of interest (ROI; approximately 4–6 cm²) in the liver tissue while avoiding nonhepatic parenchyma. The primary ROI contained a large number of secondary ROIs (25 × 25 pixels) to maintain the precision of statistical analysis. Sample variance σ² of image gradient (a texture feature related to the amount of edge structures) was calculated in secondary ROIs in a roster scan fashion. A theoretical derivation was presented to estimate population variance σ02~ of image gradient across the primary ROI from mean gradient µ of secondary ROIs. The χ² (χ² = σ²/σ02~) was computed at each secondary ROI, forming a χ² map of liver tissue heterogeneity. A cut-off value was optimized from datasets in the training group by comparing to the fibrosis grades determined by biopsy. This cut-off value was then applied to the datasets in the validation group to convert the χ² maps into binary images, from which fibrosis area fractions (fraction of fibrosis area to the total area of the primary ROI) were calculated and entered in a statistical analysis.

Results

In the training group, the optimal setting was found to be Tχ2= 6.0, which resulted a maximum discrimination of F0–3 vs F4–6: p < 0.0001, area under curve = 0.985, sensitivity = 93.7%, specificity = 93.3%. When this setting was applied to the datasets in the validation group, a distinct separation was seen between the two classes (p < 0.0001). F0–3 class had an average fibrosis area fraction of 4.7% (1.7%−11.4%), whereas the F4–6 class had an average fibrosis area fraction of 17.3% (9.8%−29.6%). A strong correlation was demonstrated between the fibrosis area fraction and histological fibrosis grade determined by biopsy (area under curve = 0.89, sensitivity = 87.9%, specificity = 90.3%).

Conclusion

The presented method is a promising noninvasive tool for distinguishing normal or low-grade liver fibrosis (F0–3) and advanced liver fibrosis or cirrhosis (F4–6) from routine ultrasound images. These findings support the further development of texture heterogeneity analysis, particularly fibrosis area fraction, as a quantitative biomarker for distinguishing various liver fibrosis grades.

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